"Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory, and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis and it teaches how ML and DL algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy, and pathology and so forth. It also covers common research problems in medical image analysis and their challenges while focussing on aspects of deep learning and machine learning for combating COVID-19. It also includes pertinent case studies. This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging"--